class_docstr: The main class of the dynamic linear model.
  
  
  This is the main class of the Bayeisan dynamic linear model.
  It provides the modeling, filtering, forecasting and smoothing
  function of a dlm.
  The dlm use the @builder to construct the @baseModel based on user supplied
  @components and then run @kalmanFilter to filter the result.
  
  Example 1:
      >>> # randomly generate fake data on 1000 days
      >>> import numpy as np
      >>> data = np.random.random((1, 1000))
      >>> # construct the dlm of a linear trend and a 7-day seasonality
      >>> myDlm = dlm(data) + trend(degree = 2, 0.98) + seasonality(period = 7, 0.98)
      >>> # filter the result
      >>> myDlm.fitForwardFilter()
      >>> # extract the filtered result
      >>> myDlm.getFilteredObs()
  
  Example 2 (fit a linear regression):
      >>> from pydlm import dynamic
      >>> data = np.random.random((1, 100))
      >>> mydlm = dlm(data) + trend(degree=1, 0.98, name='a') +
                      dynamic(features=[[i] for i in range(100)], 1, name='b')
      >>> mydlm.fit()
      >>> coef_a = mydlm.getLatentState('a')
      >>> coef_b = mydlm.getLatentState('b')
  
  Attributes:
     data: a list of doubles of the raw time series data.
           It could be either the python's built-in list of
           doubles or numpy 1d array.
docstr: Turn on the stable mode, i.e., using the renewal strategy.
  
  Indicate whether the renew strategy should be used to add numerical
  stability. When the filter goes over certain steps,
  the information contribution of the previous data has decayed
  to minimum. In the stable mode, We then ignore those days and
  refit the time series starting from current - renewTerm, where
  renewTerm is computed according to the discount. Thus,
  the effective sample size of the dlm is twice
  renewTerm. When discount = 1, there will be no renewTerm,
  since all the information will be passed along.
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code: def stableMode(self, use=True):
      """Turn on the stable mode, i.e., using the renewal strategy.
  
      Indicate whether the renew strategy should be used to add numerical
      stability. When the filter goes over certain steps,
      the information contribution of the previous data has decayed
      to minimum. In the stable mode, We then ignore those days and
      refit the time series starting from current - renewTerm, where
      renewTerm is computed according to the discount. Thus,
      the effective sample size of the dlm is twice
      renewTerm. When discount = 1, there will be no renewTerm,
      since all the information will be passed along.
      """
      # if option changes, reset everything
      if self.options.stable != use:
          self.initialized = False
  
      if use is True:
          self.options.stable = True
      elif use is False:
          self.options.stable = False
      else:
          raise ValueError("Incorrect option input")
  
imports: from copy import deepcopy
  from numpy import matrix
  from pydlm.predict.dlmPredictMod import dlmPredictModule
  from pydlm.access.dlmAccessMod import dlmAccessModule
  from pydlm.tuner.dlmTuneMod import dlmTuneModule
  from pydlm.plot.dlmPlotMod import dlmPlotModule
label_desc: Use image node_id 16 for calling the dlm obj's underlying member method code with example usage: obj.stableMode(use) without return types